{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"neptune-api","slug":"neptune-api","name":"Neptune API","type":"api","url":"https://docs.neptune.ai","page_url":"https://unfragile.ai/neptune-api","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"neptune-api__cap_0","uri":"capability://automation.workflow.distributed.experiment.logging.with.multi.process.synchronization","name":"distributed experiment logging with multi-process synchronization","description":"Logs experiment metadata (metrics, configs, artifacts) from multiple concurrent processes using a context manager pattern (`with Run()`) that handles async writes to Neptune's backend. Supports step-indexed metrics, configuration snapshots, and binary artifacts (images, audio, video, files) with implicit serialization. Designed for distributed training environments where multiple workers log simultaneously without blocking.","intents":["Log metrics and configs from a distributed training job across multiple GPUs/nodes without blocking training loops","Capture experiment metadata (hyperparameters, model checkpoints, loss curves) in a single centralized location for a team","Track multiple parallel experiments running on the same cluster with isolated run contexts"],"best_for":["ML teams running distributed training at scale (multi-GPU, multi-node setups)","Researchers managing 10+ concurrent experiments and needing centralized tracking","Organizations migrating from local experiment tracking (spreadsheets, tensorboard) to team-based platforms"],"limitations":["No documented maximum file size for artifact uploads — risk of unbounded storage usage","Async logging means metrics may not be immediately visible in web UI — eventual consistency model","Python-only SDK — no native support for PyTorch Lightning, TensorFlow, or other frameworks beyond manual logging","No built-in batching API — each log call is a separate write operation, potential throughput bottleneck at scale","Context manager pattern requires explicit `with Run()` block — cannot be used with long-running services without manual run lifecycle management"],"requires":["Python 3.7+","neptune-scale package (version 3.20251215 or later)","Valid Neptune API key (format and authentication mechanism undocumented)","Network connectivity to Neptune backend (no offline mode documented)"],"input_types":["numeric metrics (float, int, NaN, infinity)","configuration dictionaries (JSON-serializable)","image files (format unspecified)","audio files (format unspecified)","video files (format unspecified)","generic binary files"],"output_types":["run metadata stored in Neptune backend","async acknowledgment (implementation details unknown)"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_1","uri":"capability://search.retrieval.metadata.querying.and.filtering.with.extended.regex.syntax","name":"metadata querying and filtering with extended regex syntax","description":"Queries logged experiment runs using the `neptune-query` package with support for filtering across metrics, configs, and run metadata using extended regex syntax. Enables cross-project searches and retrieval of experiment metadata without requiring web UI navigation. Returns structured run objects with access to all logged artifacts and metrics.","intents":["Find all experiments where learning rate was between 0.001 and 0.01 and accuracy exceeded 0.95","Retrieve the best-performing model checkpoint from 100+ runs based on validation metric","Export experiment metadata (hyperparameters, metrics) for statistical analysis or reporting"],"best_for":["Data scientists building automated hyperparameter optimization pipelines","ML engineers creating reproducibility scripts that query historical experiments","Teams needing programmatic access to experiment data for meta-analysis or reporting"],"limitations":["Extended regex syntax not fully documented — unclear which regex features are supported vs. unsupported","No documented query performance characteristics — risk of slow queries on large experiment sets (1000+ runs)","Query API returns full run objects — no pagination or streaming results documented, potential memory issues with large result sets","No aggregation functions documented (e.g., max, min, mean across runs) — requires client-side computation","Cross-project queries may have undocumented latency or permission implications"],"requires":["Python 3.7+","neptune-query package (version 3.20251215 or later)","Valid Neptune API key with read permissions","Knowledge of extended regex syntax (documentation incomplete)"],"input_types":["filter expressions (regex strings)","project identifiers (string)","run metadata keys (string)"],"output_types":["run objects with nested metric/config data","structured metadata dictionaries"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_10","uri":"capability://automation.workflow.experiment.run.lifecycle.management.with.context.manager.pattern","name":"experiment run lifecycle management with context manager pattern","description":"Manages experiment run lifecycle using Python context manager (with statement) pattern, automatically initializing run state on entry and flushing/closing on exit. Context manager ensures proper resource cleanup and backend synchronization even if training code raises exceptions, preventing data loss and orphaned connections.","intents":["Ensure all logged metrics are flushed to backend even if training crashes or raises exception","Automatically manage Neptune connection lifecycle without manual initialization/cleanup code","Enable clean experiment teardown with proper resource deallocation"],"best_for":["Python-based ML training scripts using standard exception handling patterns","Teams wanting minimal boilerplate code for Neptune integration","Researchers prioritizing code simplicity and readability"],"limitations":["Context manager pattern is Python-specific — no equivalent for other languages","Nested context managers not documented — unclear if multiple runs can be active simultaneously","Exception handling behavior not specified — unclear if exceptions are suppressed or propagated","Timeout or forced-close behavior not documented — unclear what happens if backend sync hangs"],"requires":["Python 3.7+","neptune-scale package","Training code structured with 'with' statement"],"input_types":["run configuration dict"],"output_types":["Run context object with log_metrics() and log_configs() methods"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_11","uri":"capability://image.visual.png.export.of.visualizations.for.offline.sharing","name":"png export of visualizations for offline sharing","description":"Exports metric charts and dashboards as PNG images with embedded metadata, enabling offline sharing via email, Slack, or documentation without requiring Neptune account access. Export preserves chart styling, legends, and multi-run overlays, generating publication-ready visualizations.","intents":["Share experiment results in email or Slack messages without requiring recipients to access Neptune","Include metric charts in research papers or technical reports as static images","Create offline documentation of experiment results for compliance or archival purposes"],"best_for":["Teams with non-technical stakeholders who don't have Neptune accounts","Researchers publishing results and needing static visualizations","Organizations with offline-first documentation requirements"],"limitations":["Export format limited to PNG — no vector formats (SVG, PDF) documented","Interactive features (zoom, pan, hover) lost in static export","Export resolution and DPI not documented","Batch export of multiple charts not documented — appears to be single-chart export only"],"requires":["Neptune account with run access","Web browser for chart viewing and export UI"],"input_types":["chart or dashboard selection"],"output_types":["PNG image file with embedded metadata"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_2","uri":"capability://image.visual.multi.metric.visualization.and.side.by.side.experiment.comparison","name":"multi-metric visualization and side-by-side experiment comparison","description":"Web-based visualization dashboard that renders logged metrics as interactive charts, with side-by-side comparison view showing metric deltas between selected runs in diff format. Supports custom views with filtered run tables, persistent shareable links for charts/dashboards, and PNG export of visualizations. Built on Neptune's web app (version 3.20251215).","intents":["Visualize training loss curves across 5 different model architectures to identify convergence patterns","Share a comparison of two hyperparameter configurations with stakeholders via a persistent link","Export a chart showing validation accuracy over epochs as PNG for a research paper or presentation"],"best_for":["Research teams presenting experiment results to non-technical stakeholders","ML engineers doing rapid hyperparameter tuning and needing quick visual feedback","Cross-functional teams (product, research, engineering) collaborating on model selection"],"limitations":["Visualization types not documented — unclear if heatmaps, 3D plots, or custom chart types are supported","Real-time update latency unknown — may lag behind actual metric logging by seconds/minutes","PNG export resolution and quality not specified — may be unsuitable for high-resolution printing","Custom dashboard widget types not documented — limited to pre-built chart types","No programmatic chart generation API — dashboards must be created via web UI"],"requires":["Web browser with JavaScript enabled","Neptune account with project access","Metrics logged via neptune-scale package","Network connectivity to Neptune web app"],"input_types":["logged metrics (numeric time series)","run selections (UI-based)","filter expressions (UI-based)"],"output_types":["interactive web charts","PNG image files","persistent shareable URLs","diff-format comparison tables"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_3","uri":"capability://data.processing.analysis.configuration.snapshot.and.hyperparameter.tracking","name":"configuration snapshot and hyperparameter tracking","description":"Captures experiment configurations (hyperparameters, model architecture details, dataset paths) as immutable snapshots via `log_configs()` method, storing them alongside metrics for reproducibility. Configurations are queryable and comparable across runs, enabling hyperparameter sensitivity analysis and reproducibility audits without manual parameter logging.","intents":["Log all hyperparameters (learning rate, batch size, optimizer, regularization) at experiment start for reproducibility","Compare hyperparameter values across runs that achieved different performance levels","Audit which exact configuration was used for a model that was deployed to production"],"best_for":["ML teams requiring strict reproducibility and audit trails for regulated industries","Researchers conducting hyperparameter sensitivity studies across many configurations","DevOps/MLOps engineers managing model versioning and configuration drift detection"],"limitations":["Configuration schema not enforced — no validation that required hyperparameters are logged","No versioning for configs — if hyperparameters change mid-experiment, unclear if old values are preserved","No diff highlighting for config changes — comparison requires manual inspection of values","Maximum config size not documented — risk of logging oversized configuration objects","No config templates or defaults — each experiment must explicitly log all parameters"],"requires":["Python 3.7+","neptune-scale package","Configuration data as Python dictionary (JSON-serializable)"],"input_types":["configuration dictionaries (nested dicts, lists, primitives)","hyperparameter values (strings, numbers, booleans)"],"output_types":["immutable config snapshots stored in Neptune backend","queryable config metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_4","uri":"capability://automation.workflow.collaborative.dashboards.and.report.generation","name":"collaborative dashboards and report generation","description":"Creates shareable dashboards combining multiple charts, filtered run tables, and custom widgets. Generates collaborative reports with persistent URLs that can be shared with team members without requiring them to have Neptune accounts. Supports real-time updates as new experiments are logged, enabling live monitoring of ongoing training jobs.","intents":["Create a dashboard showing the top 10 best-performing models by validation accuracy for the team to monitor","Generate a weekly report comparing this week's experiments to last week's baseline and share with stakeholders","Share a live dashboard with a collaborator that updates in real-time as a distributed training job logs new metrics"],"best_for":["ML teams with non-technical stakeholders (product managers, executives) who need experiment visibility","Research groups publishing results and needing reproducible, shareable experiment summaries","Cross-functional teams (research + engineering) coordinating on model selection and deployment"],"limitations":["Dashboard widget types not documented — unclear what visualizations are available beyond charts","Real-time update frequency not specified — may lag by seconds/minutes behind actual logging","No programmatic dashboard creation API — dashboards must be built via web UI","Permission model for shared dashboards not documented — unclear if recipients can modify or only view","No scheduled report generation — reports must be manually created and shared","Dashboard storage limits not documented — unclear if there are limits on number of dashboards per project"],"requires":["Neptune account with project access","Web browser","Metrics/runs logged via neptune-scale package","Recipient email address (for sharing, if applicable)"],"input_types":["chart selections (UI-based)","run filters (UI-based)","custom widget configurations (UI-based)"],"output_types":["persistent shareable dashboard URLs","interactive web dashboards","report documents (format unspecified)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_5","uri":"capability://data.processing.analysis.artifact.versioning.and.binary.file.storage","name":"artifact versioning and binary file storage","description":"Stores binary artifacts (model checkpoints, images, audio, video, files) alongside experiment metadata with implicit versioning by run and step. Artifacts are queryable and retrievable via the neptune-query API, enabling model registry functionality without requiring separate artifact storage systems. Supports arbitrary file types with automatic serialization.","intents":["Save model checkpoints at each epoch and retrieve the best checkpoint based on validation metric","Store training visualizations (loss curves, confusion matrices) as images for later review","Archive audio samples generated by a text-to-speech model for quality auditing"],"best_for":["ML teams managing model versioning without external artifact stores (S3, GCS)","Research groups archiving all experiment outputs (checkpoints, visualizations, samples) in one place","Small-to-medium teams where centralized artifact storage simplifies infrastructure"],"limitations":["Maximum file size not documented — risk of storage quota exceeded without warning","Artifact retrieval API not documented — unclear how to download artifacts programmatically","No deduplication or compression — identical artifacts stored multiple times consume full storage","No artifact lifecycle policies — no automatic cleanup of old checkpoints or versioning limits","Artifact format support not fully documented — unclear which image/audio/video formats are supported","No streaming download API — entire artifact must be loaded into memory"],"requires":["Python 3.7+","neptune-scale package","Binary file data (images, audio, video, or generic files)","Sufficient Neptune storage quota"],"input_types":["image files (format unspecified)","audio files (format unspecified)","video files (format unspecified)","generic binary files"],"output_types":["artifact metadata (run, step, file path)","binary file data (via query API)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_6","uri":"capability://search.retrieval.run.filtering.and.custom.view.creation","name":"run filtering and custom view creation","description":"Creates filtered views of experiment runs using metadata-based filters (metrics, configs, run names) with persistent storage of filter definitions. Custom views appear as tabs in the runs table, enabling teams to organize experiments by project, model type, or performance tier without duplicating data. Filters support regex matching and multi-criteria AND/OR logic.","intents":["Create a 'production-ready' view showing only runs with accuracy > 0.95 and inference latency < 100ms","Filter experiments by model architecture (ResNet, VGG, Transformer) to compare variants","Save a view of 'failed experiments' (runs with NaN loss) for debugging"],"best_for":["ML teams with 100+ concurrent experiments needing organizational structure","Research groups comparing model variants across many runs","DevOps teams managing model selection and promotion workflows"],"limitations":["Filter logic not fully documented — unclear if complex nested AND/OR conditions are supported","View persistence scope not documented — unclear if views are team-wide or user-specific","No filter templates or presets — each view must be manually configured","Filter performance not documented — may be slow on large experiment sets (1000+ runs)","No view versioning — filter definitions cannot be rolled back if accidentally modified"],"requires":["Neptune account with project access","Runs logged with queryable metadata (metrics, configs)","Web browser"],"input_types":["filter expressions (metric ranges, config values, regex patterns)","view names (strings)"],"output_types":["filtered run tables","persistent view definitions","run count summaries"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_7","uri":"capability://data.processing.analysis.step.indexed.metric.logging.with.automatic.time.series.tracking","name":"step-indexed metric logging with automatic time-series tracking","description":"Logs metrics with explicit step indices (epoch, iteration, batch number) enabling automatic time-series construction without manual timestamp management. Supports logging multiple metrics per step, NaN and infinity values, and implicit metric aggregation across distributed processes. Step indices enable metric alignment across runs with different training lengths.","intents":["Log training loss at each batch without manually tracking timestamps or epoch numbers","Compare validation accuracy curves across runs that trained for different numbers of epochs","Handle NaN loss values (e.g., from numerical instability) without breaking metric tracking"],"best_for":["ML engineers training models with standard epoch/iteration-based workflows","Researchers comparing convergence rates across different training schedules","Teams using distributed training where step synchronization is critical"],"limitations":["Step index type not documented — unclear if only integers are supported or if floats are allowed","Step alignment across distributed processes not documented — unclear how Neptune handles out-of-order step logging","No automatic metric aggregation — if multiple processes log the same metric at the same step, unclear which value is stored","No metric downsampling — logging every batch may create large time-series with 10000+ points","Step semantics not enforced — no validation that steps are monotonically increasing"],"requires":["Python 3.7+","neptune-scale package","Numeric metric values (float, int, NaN, infinity)","Step index values (type unspecified)"],"input_types":["numeric metrics (float, int)","special values (NaN, infinity)","step indices (integer, presumably)"],"output_types":["time-series metric data stored in Neptune backend","queryable metric metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_8","uri":"capability://automation.workflow.real.time.multi.team.collaboration.with.role.based.access.control","name":"real-time multi-team collaboration with role-based access control","description":"Enables multiple team members to view and interact with shared experiments simultaneously with role-based access control (RBAC) determining read/write/admin permissions. Supports real-time updates as new metrics are logged, allowing team members to monitor ongoing training jobs without polling. Persistent sharing links enable asynchronous collaboration across time zones.","intents":["Share a live training dashboard with a colleague in a different timezone who can view updates without logging in","Grant read-only access to a stakeholder so they can view experiment results but cannot modify runs","Revoke a team member's access to a project when they leave the team"],"best_for":["Distributed ML teams across multiple time zones needing asynchronous collaboration","Organizations with non-technical stakeholders (product, business) who need experiment visibility","Teams with strict access control requirements (different permissions for researchers vs. engineers)"],"limitations":["RBAC granularity not documented — unclear if permissions are project-level or run-level","Real-time update mechanism not documented — unclear if WebSockets or polling is used","Sharing link expiration not documented — unclear if links are permanent or time-limited","Audit logging not documented — no visibility into who accessed what and when","No fine-grained permissions — unclear if you can grant read access to specific runs but not others","Invitation workflow not documented — unclear how to add team members or revoke access"],"requires":["Neptune account with team/project setup","Team members with valid Neptune accounts (or public sharing links)","Web browser","Network connectivity to Neptune backend"],"input_types":["team member email addresses (for invitations)","permission levels (read, write, admin — presumably)","shareable link generation (UI-based)"],"output_types":["persistent shareable URLs","real-time updated dashboards","access control lists"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__cap_9","uri":"capability://automation.workflow.distributed.training.process.isolation.and.run.context.management","name":"distributed training process isolation and run context management","description":"Manages isolated run contexts for each training process using the context manager pattern (`with Run()`), preventing metric/config collisions when multiple processes log simultaneously. Each process gets a unique run ID, and Neptune handles async writes without requiring explicit process synchronization or queue management. Supports both single-process and multi-GPU/multi-node training.","intents":["Run 4 distributed training processes on a single machine without metrics from one process overwriting another's","Launch a hyperparameter sweep with 100 parallel jobs, each logging to its own isolated run","Track metrics from a multi-GPU training job where each GPU logs independently"],"best_for":["ML teams running distributed training on multi-GPU or multi-node clusters","Researchers conducting large-scale hyperparameter sweeps with many parallel jobs","DevOps engineers managing automated training pipelines with dynamic process spawning"],"limitations":["Run ID generation mechanism not documented — unclear if IDs are UUIDs, sequential, or user-specified","Process synchronization guarantees not documented — unclear if out-of-order writes are handled correctly","No explicit process coordination API — teams must manage process lifecycle externally (e.g., via Kubernetes, Ray)","Context manager pattern requires explicit `with Run()` block — incompatible with long-running services or REPL-based workflows","No automatic process discovery — Neptune does not detect or manage spawned processes"],"requires":["Python 3.7+","neptune-scale package","Network connectivity to Neptune backend from all processes","Valid Neptune API key accessible to all processes"],"input_types":["experiment name (string)","run metadata (optional)"],"output_types":["isolated run context objects","unique run IDs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"neptune-api__headline","uri":"capability://data.processing.analysis.ml.experiment.tracking.and.model.registry.api","name":"ml experiment tracking and model registry api","description":"Neptune API is an experiment tracking and model registry solution designed for teams managing multiple machine learning experiments, offering features for logging, comparison, and collaboration throughout the ML model 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functions documented (e.g., max, min, mean across runs) — requires client-side computation","Cross-project queries may have undocumented latency or permission implications","builder identity is not verified yet","no observed match outcomes 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